How to Build an NBA Championship Team

This year’s MIT Sloan Sports Analytics Conference, the summit of the wonkiest minds in sports, hardly resembled its inaugural event, in 2007, when 175 geeks gathered in classrooms on MIT’s campus to discuss their budding cottage industry. Having expanded recently to two days, and moved to a convention center in Boston’s Back Bay neighborhood, there were far fewer pocket protectors among the 2,200 attendees than league commissioners, team ecutives, and even cerebral athletes. The panels with recognizable names took place on elevated stages in spaces as vast as airplane hangars. Everyone in the audience was looking for an edge on the competition, fully aware that the next Moneyball-like, game-changing idea would probably come from someone sitting next to them.

Meanwhile, the heart of the conference still pumped through two smaller rooms at the end of a hallway, adjacent to a game area with a Pop-A-Shot. One room was devoted to research presentations. The other was reserved for a series of talks called Evolution of Sport. The EOS talks were billed as the "opportunity to present a message, an idea or a revolutionary thought that could someday change the face of sport"—TED talks for an audience fluent in ESPN. According to conference organizers, the EOS edicts were to be bold, unique, inventive, analytical, concise, respectful, curious, humorous, honest, and, most of all, inspiring. Over eighty EOS were submitted for consideration; eleven were chosen to inspire.

The only undergraduate in that group was a Stanford University senior named Muthu Alagappan. He was presenting on behalf of Ayasdi, a company run by Stanford mathematicians, whose proprietary software is used by physicians, environmentalists, and the government to understand cancer, diabetes, and oil spills. Muthu had used it to scheme the NBA.

Muthu grew up in Houston, and when we were introduced in the convention center’s hallway before his presentation, which he called "From 5 to 13: Redefining the Positions in Basketball," he told me he was hoping to meet Houston Rockets general manager Daryl Morey, the conference co-chair. Morey is such a rock star around MIT that Bill Simmons once nicknamed him Dork Elvis. He was one of the first basketball ecutives to bring to an NBA front office the sort of quantitative analysis that revolutionized baseball in the last decade, and because of his organizing role with the conference, he also has emerged as the public face of basketball geeks.

Muthu didn’t find Morey before his EOS talk, but he did have an intrigued audience when he finally made his pitch for the future of sports. He spoke with a polished, unassuming authority, like a hospital resident who rides a disarming smile into his operating room. In fact, he was three months from graduating with a degree in biomechanical engineering, and planning to stay in Palo Alto for medical school. When he stuffed his hands into the pants pockets of a dark suit, which he wore without a tie, Muthu opened with a parable about medicine. Almost two thousand years ago, he said, the Roman physician Galen theorized that all illnesses could be categorized by fluids. Galen is still admired as a medical visionary, but as science evolved, doctors quickly understood the simplicity of his logic.

"Physicians began to realize that disease classification was actually much more complex," Muthu said. "By doing so, for the first time in the history of the world, physicians became more than just spiritual healers. They could actually save lives." He paused, not too dramatically, and made his point: "Today, I believe basketball is on the verge of a similar revolution."

Over the next half-hour he explained why. Muthu’s conclusion was that NBA players account for thirteen positions, not just the five traditional roles, and he thought this discovery wasn’t just academic. It could alter the way all basketball players are evaluated and make people like Morey fundamentally rethink the way they assemble teams.

Not long afterward, Morey took the stage in the spacious ballroom to present the conference’s awards. Bill James, the godfather of sabermetrics, who published advanced baseball research as early as the 1970s and inspired the last three decades of innovation in sports, won for lifetime achievement. The Tampa Bay Rays scored the award for best organization. The reigning NBA champion Dallas Mavericks were also honored. And then Morey announced to polite applause the prize for the conference’s best EOS talk. The winner was Muthu Alagappan.

Billy Beane, the Oakland Athletics general manager lionized in Michael Lewis’s Moneyball, once turned down an athletic scholarship from Stanford and instead was drafted in the first round out of high school by the New York Mets. Four years later, he made the big leagues.

No one would ever confuse Muthu for Billy Beane. "As an Indian kid in Houston, basketball is what you do," Muthu said. "I tried out for my middle-school team and didn’t make it. That was the start of me realizing I was not made to play basketball." Instead he played tennis and thrived nationally as a debater in high school. At Stanford, he danced Raas and Bhangra, traditional Indian styles, and biked to class. When he did find time for basketball, about once a week, Muthu played point guard. There was no other choice for a 5-foot-9 guy with a slight build. But he also felt out of position, since ball-handling was not his strength. Even in pickup games, he thought, there was an unrecognized nuance to basketball positions.

Muthu began his deep dive into NBA positions one Friday last summer, during his internship with Ayasdi, by picking seven rudimentary statistics on Yahoo! Sports for every NBA player: points, rebounds, assists, steals, turnovers, fouls, and blocks. Then he adjusted them for playing time. Within three hours, he had a graphic with dense groups of color-coded nodes, representing last season’s NBA players, connected by lines, expressing statistical affinities. The nodes were the players, the groups were the newfound positions, and the lines linked statistically similar players. This was his groundbreaking similarity network of NBA players. It looked like a postcard from a molecular biology convention.

"We expected to see five categories, or five flares, corresponding to the five positions of basketball. We actually found something much more interesting: We found that there’s thirteen positions in the NBA," he said at the MIT conference. "And for each position, for the first time, I can topologically and mathematically define what it means to play that position. I can tell you which players in the NBA play which position. And I can tell you who epitomizes the position best."

The first thing to know about the thirteen NBA positions—Muthu labeled them offensive ball-handler, defensive ball-handler, combo ball-handler, shooting ball-handler, role-playing ball-handler, 3-point rebounder, scoring rebounder, paint protector, scoring paint protector, role player, NBA first team, NBA second team, and one-of-a-kind—is that the idea of thirteen NBA positions is a misnomer. Anyone who’s ever watched basketball knows that there are more than thirteen positions, and not even Isaiah Thomas would put together a team based solely on five positions. Indeed, the best basketball players are like soccer midfielders: They can function anywhere on the court, they make their teams better, and they’re not defined by position. But Muthu’s positions weren’t all that rigid. Tony Parker is an offensive ball-handler, which separates him from John Wall, considered a combo ball-handler, not based on anything stylistic but solely because of their statistics. Tyson Chandler, the NBA’s Defensive Player of the Year, is a paint protector who specializes on one side of the floor. Kevin Love and Blake Griffin are actually scoring paint protectors. Some players are in a league of their own: Kevin Durant and LeBron James, for example, are NBA first-teamers. Derrick Rose and Dwight Howard are one-of-a-kinders whose statistical combinations make them NBA outliers.

But the insights from Muthu’s similarity network were far more profound than what meets the eye. Muthu noticed that intrinsic talent alone was not the reason Carmelo Anthony, Dirk Nowitzki, and Chris Bosh clustered together. It was their statistical profiles that linked them as "scoring rebounders," one of the most valuable player types in all of basketball. Devin Ebanks wasn’t considered so valuable. Ebanks is a forward in his second year with the Los Angeles Lakers who, on the afternoon of Muthu’s talk in March, had scored exactly 32 points in 36 Lakers games. And yet Muthu insisted that the comparisons for Ebanks, another scoring rebounder based on his metrics, were not just not dismal. They actually made him one of the most promising prospects in the game.

The reaction to this curious insight was similar to the way hardened baseball scouts condescended to early sabermetricians. He’s not undervalued. He’s Devin Ebanks! Then something astonishing happened. A few weeks later, when Kobe Bryant missed time with an injury and Metta World Peace was out because of a suspension, they were replaced in the starting lineup by none other than Devin Ebanks. And in Ebanks’ first start since December, the player Muthu’s model compared to some of the game’s all-stars scored 12 points in 32 minutes, both career highs. Ebanks started every regular-season and postseason game when Bryant or World Peace sat in street clothes, and he reached double figures on four occasions. The model wasn’t suggesting that Ebanks was Dirk Nowitzki’s equal, and even after his starting stint, it was impossible to know whether Ebanks could ever sustain his per-minute stats. But Muthu had gone out on a limb by proposing that Ebanks could play the role of "scoring rebounder" at a low price, providing more bang for a team’s buck. For six weeks, at least, Ebanks proved him right.

Muthu had been unsure of his original findings, so he had explored the data on his own for about a week. Before long, in addition to briefing his bosses on what he had discovered, he contacted some basketball geeks out of the blue. He was encouraged when most of them replied. "That’s when we realized we were onto something," he says.

One of the expert quants was Ken Pomeroy, the proprietor of KenPom.com, the leading site for college hoops analytics. Pomeroy was compelled to offer feedback, he says, because Muthu’s was a fresh way of attacking a stale problem. Pomeroy also had thought about the way players were classified into positions, and he believes the next step for NBA front offices is figuring out how to measure "fit," or the way pieces of one team interact with each other. "There aren’t many ways to get ahead anymore, but this is one of those things that could allow someone to get ahead," he said. "It’s an interesting concept—a new way of thinking about things." Buoyed by that affirmation, Muthu applied, and was accepted, to speak at MIT’s sports analytics conference.

McKinsey Global Institute, the research arm of the consulting firm McKinsey & Company, released a report last year about big data. The study concluded that big data, which is exactly what it sounds like, can unlock the secrets of every industry imaginable. It improves decision-making and increases efficiency. It is, McKinsey says, the "next frontier for innovation, competition and productivity."

The problem with big data, though, is figuring out how to make sense of it. Ayasdi was founded by Stanford academics in 2008, and its founders spent three and a half years developing Iris, the company’s signature software, for that express purpose. What Iris does, basically, is turn complicated spreadsheets into intelligible visualizations, which then lead to breakthroughs. "Every data set analyzed by Iris has led to identification of previously undiscovered knowledge," the company boasts on its website. Ayasdi touts examples of Iris’s success in understanding breast cancer mutations, diabetes types, and leukemia patients.

Gunnar Carlsson, Ayasdi’s co-founder, is a Stanford mathematics professor from the university’s Applied and Computational Algebraic Topology research group who has made a career from studying topology, the very kind of analysis that might allow Muthu to unlock secrets of the NBA. Carlsson’s expertise means he also spends time explaining what topology is. "In general, topology is the part of mathematics that deals with representing data in space," he told me. "It’s viewed as this esoteric thing, because it’s trying to measure something a little bit more nebulous than just a number."

As the supply of data has swelled in the last two decades, Carlsson said, so has the demand for topology. The thing that distinguishes his field, and Ayasdi’s software, is that it allows even brilliant mathematical minds, computer scientists, and biologists to essentially work in reverse. "We think of it as the ’Jeopardy!’ model for data analysis," Carlsson said. "You don’t want to have to ask a question. You want to look at your answer first, then ask the question."

Eight of the company’s 14 employees boast Ph.D’s in math, science, or engineering, and Muthu was the first undergraduate on Ayasdi’s payroll. He found his internship through Stanford’s Mayfield Fellows Program, which places a dozen aspiring entrepreneurs every year with Silicon Valley staples like Facebook, Google, and Twitter, and counts among its alumni Kevin Systrom and Mike Krieger, the founders of Instagram. Muthu was hired on the sales side of the company but began plugging data into the software right away. "You get this network with a lot of answers embedded in it, and you can ask an infinite number of questions," he said. "But it’s about asking the right question."

And sticking up for Devin Ebanks wasn’t what Muthu set out to do. That is, how to find an undervalued player wasn’t the question worth asking. Not really, anyway. The shape of the data actually answered a far more fascinating question: How could this knowledge help NBA teams win championships?

The categories that Muthu unveiled weren’t basketball positions so much as they were assets. And in order to fully understand them, you had to visualize them. To do so, at the MIT conference, Muthu flashed slides of the topological networks of two different NBA teams last year. The first team was balanced: two scoring rebounders, two paint protectors, two role players and a variety of ball-handlers, spread out in a way that would seem aesthetically pleasing to someone who doesn’t know a block from a charge. The second team was messier: too many ball-handlers and not even one scoring rebounder among the types scattered in the same places. More than anything, the second team just looked wrong, like a car with three wheels about to tip over. Muthu cycled through the networks once more for effect before he revealed those teams. The first was the Dallas Mavericks, which won last year’s NBA championship. The second was the Minnesota Timberwolves, which won 17 games, the fewest in the NBA last year.

"The exciting thing is that within seconds we can do this for every team in the NBA last year," Muthu said. "And also for every team in NBA history."

Muthu wasn’t finished yet. He promised Ayasdi’s software could handle the biggest of big data—the stronger the matrix, the richer the model. Instead of plugging in basketball’s seven basic statistics, a progressive NBA front office, perhaps the Oklahoma City Thunder’s, might use player efficiency ratings or win shares and watch the data’s shape evolve in real time. The software could function as a metric for coaches, specific lineups, or plus-minus matchups. In the same way Ayasdi researchers looked at cancer genomes, Muthu’s method, when used properly, could take existing statistics and make NBA teams see themselves and their opponents unlike ever before. The software’s untapped potential tantalized like a freakish vertical jump.

"The network shown is almost a proof of concept," Muthu said. "As the data gets better, the network gets smarter." In other words, Muthu was talking about a tool with the power to change the way sports teams are constructed. What’s more, he was making it sound like a no-brainer.

About halfway through "Moneyball," Michael Lewis draws a line for the first time from the U.S. financial markets of the 1980s to the process of scouting baseball players in the 2000s. Actually, he writes, the strategy of exploiting undervalued assets was exactly what derivatives traders did three decades before Beane. Those people "were highly trained mathematicians and statisticians and scientists who had abandoned whatever they were doing at Harvard or Stanford or MIT to make a killing on Wall Street," Lewis wrote. "The fantastic sums of money hauled in by the sophisticated traders transformed the culture on Wall Street, and made quantitative analysis, as opposed to gut feel, the respectable way to go about making bets in the market." Those traders spawned the quants that made way for Beane, who in turn blazed a path in sports for his acolytes. And what happened next?

"The chief social consequence was to hammer into the minds of a generation of extremely ambitious people a new connection between ’inefficiency’ and ’opportunity,’" Lewis wrote, "and to reinforce an older one, between ’brains’ and ’money.’"

Muthu got his first taste of media attention after the conference, with coverage in The New York Times, Wired, and the seminal basketball blog TrueHoop. About a month later, his work was once again featured and debated in The Wall Street Journal, Slate, and Kottke. Some eggheads still scoffed at the name Devin Ebanks, as Muthu expected, since it was no secret that much of his research was still more theoretical than practical. But something Muthu said must have been insightful, if not downright inspiring. He had won an award!

I knew that Muthu did meet Morey eventually, considering Morey presented him with his prize, but what I really wanted to know when I spoke to Muthu after his trip to MIT was whether any NBA teams had approached him about working with Ayasdi. As it turned out, they had. By the beginning of May, Muthu said, the company had spoken with representatives from at least four NBA teams about possible partnerships, and one Major League Baseball team had expressed interest in applying topology tools to its troves of data. Muthu was planning to take the summer between graduation and medical-school orientation to work on other ways he could connect with professional sports teams.

So what was true in business three decades ago—the path from inefficiency to opportunity, and then from brains to money—is still pretty much true of sports today. There’s just one truly delicious caveat. The riches for the best and brightest Wall Street traders back then amounted to more boatloads of money than they could dream of spending. That’s not the case on this side of sports. Some private investors in Silicon Valley approached Ayasdi about making money off sports, but Muthu remains nonplussed. "The thing I’m most interested in is not gambling or predictions or fantasy," he told me, "but in-game strategy and GM-type decisions." It was the latest example of the brains in sports chasing another kind of fabulous wealth. The wins are their spoils.

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